Senior Analytics Engineer

ZoomInfo is seeking an experienced Senior Analytics Engineer to join the Enterprise Data Engineering team. As an Analytics Engineer, you will play a pivotal role in advancing the maturity of our data organization. Working cross-functionally with Engineering, Product, Security, and business stakeholders, you’ll design, build, and own the core infrastructure and data models that power data-driven decision-making across the organization.

Responsibilities

• Design, develop, and maintain scalable analytics data models that support reporting, business intelligence, product analytics, and operational decision-making.
• Build and manage trusted data pipelines and transformation workflows using modern analytics engineering practices.
• Partner with stakeholders across Engineering, Product, Design, Strategy & Operations, and AI Platform to understand business needs and translate them into effective data solutions.
• Establish clear definitions, metrics, dimensions, and data contracts to create consistency across teams and use cases.
• Develop reusable datasets and semantic models that enable self-service analytics and reduce duplicated work.
• Improve the reliability, performance, observability, and maintainability of analytics infrastructure.
• Implement data quality checks, testing, documentation, lineage, and monitoring for critical data assets.
• Collaborate with data engineers and platform teams to optimize data architecture, warehouse performance, and transformation workflows.
• Work with analysts, data scientists, and business partners to ensure data products are intuitive, discoverable, and fit for purpose.
• Help establish best practices for analytics engineering, including version control, code review, CI/CD, documentation, and deployment standards.
• Communicate technical concepts clearly to both technical and non-technical audiences.

Requirements

• 4+ years of experience in analytics engineering, data engineering, business intelligence engineering, or a related field.
• Strong SQL skills, including experience developing complex queries and dimensional data models.
• Experience building and maintaining analytics transformations using tools such as dbt or comparable frameworks.
• Proficiency in Python or another programming language used for data processing and automation.
• Experience working with modern cloud data warehouses such as Snowflake, Databricks, BigQuery, or Redshift.
• Strong understanding of data modeling concepts, including dimensional modeling, fact and dimension tables, slowly changing dimensions, and semantic layers.
• Experience implementing data quality testing, monitoring, documentation, and observability practices.
• Demonstrated ability to partner with cross-functional stakeholders and translate ambiguous business questions into scalable data solutions.
• Strong communication, problem-solving, and organizational skills.
• A thoughtful approach to data governance, security, privacy, and responsible use of data.
• Familiarity with data cataloging and lineage tools.

Tech Stack

• Warehousing: Snowflake as the primary warehouse; BigQuery for GCP-native workloads
• Ingestion: Fivetran for SaaS-to-Snowflake pulls; Airflow (on AWS) for custom DAGs, Lambda-based extraction, and S3 staging
• Modeling: dbt for transformation and semantic layer definition
• Custom compute: Databricks for bespoke modeling work that doesn't fit cleanly into dbt/Snowflake
• Infrastructure: Terraform for managing Fivetran connections, GCP infrastructure, and AWS resources
• Version control & CI/CD: GitHub
• Consumption: Tableau dashboards; internal tools built on top of semantic data layers